A remote sensing based index for assessing long-term ecological impact in arid mined land

Large-scale mining operations in western China would accelerate the fragility and degradation to arid ecological environments. Remotely sensed monitoring and assessing of mining effects in long term change could provide sound understanding to guide mine ecological restoration and local ecosystem sustainability. This study's primary goal is to develop an arid mined land status (AMLS) index based on remote sensing data to assess ecological effects in mining areas of arid land. Furthermore, the performance of AMLS was contrasted with that of the remote sensing ecological index (RSEI) and the land surface ecological status composition index (LSESCI) in arid mines under various landscapes conditions. Eventually, the Sen + Mann-Kendall method was used to analyze the temporal and spatial land status variations in the mining effect between 2005 and 2020 based on Landsat timeseries images. The following was revealed by the results: (1) AMLS had high correlation (r > 0.65) with each factor, including bare soil flatness, dryness, land surface temperature, and slope. (2) Comparing with LSESCI and RSEI, the proposed AMLS could characterize the mining area and surrounding natural bare soil and rock with different ecological status levels in three mines, hence indicated the mining directional heterogeneity and distance attenuation of ecological status. (3) Based on time series Landsat data, the AMLS demonstrated long-term ecological variability from 2005 to 2020 due to mining and reclamation in three mines.